2016-2018 publications utilizing HyspIRI airborne campaign data and related datasets
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Keywords |
Reference |
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Fire behavior Fuels Imaging spectroscopy LIDAR AVIRIS |
Stavros, E. N., Coen, J., Peterson, B., Singh, H., Kennedy, K., Ramirez, C., & Schimel, D. (2018). Use of imaging spectroscopy and LIDAR to characterize fuels for fire behavior prediction. Remote Sensing Applications: Society and Environment, 11, 41–50. https://doi.org/10.1016/J.RSASE.2018.04.010 |
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Acid mine waste/Aqueous Ferrous Iron |
Davies, G. E., & Calvin, W. M. (2017). Quantifying Iron Concentration in Local and Synthetic Acid Mine Drainage: A New Technique Using Handheld Field Spectrometers. Mine Water and the Environment, 36(2), 299–309. https://doi.org/10.1007/s10230-016-0399-z |
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Soil properties |
Dutta, D., Kumar, P., & Greenberg, J. A. (2017). Effect of Spatial Filtering on Characterizing Soil Properties From Imaging Spectrometer Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(9), 4149–4170. https://doi.org/10.1109/JSTARS.2017.2701809 |
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Urban heat island, Land use, Urbanization, Ecosystem heterogeneity
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Crum, S. M., Shiflett, S. A., & Jenerette, G. D. (2017). The influence of vegetation, mesoclimate and meteorology on urban atmospheric microclimates across a coastal to desert climate gradient. Journal of Environmental Management, 200, 295–303. https://doi.org/10.1016/J.JENVMAN.2017.05.077 |
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MESMA Turfgrass lawns Urban trees |
Wetherley, E. B., Roberts, D. A., & McFadden, J. P. (2017). Mapping spectrally similar urban materials at sub-pixel scales. Remote Sensing of Environment, 195, 170–183. https://doi.org/10.1016/J.RSE.2017.04.013 |
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Vegetation structure |
Yao, W., van Aardt, J., & Messinger, D. (2017). On the creation of high spatial resolution imaging spectroscopy data from multi-temporal low spatial resolution imagery. In M. Velez-Reyes & D. W. Messinger (Eds.) (Vol. 10198, p. 1019814). International Society for Optics and Photonics. https://doi.org/10.1117/12.2262521 |
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Land cover, Forrest alliances |
Clark, M. L., Buck-Diaz, J., & Evens, J. (2018). Mapping of forest alliances with simulated multi-seasonal hyperspectral satellite imagery. Remote Sensing of Environment, 210, 490–507. https://doi.org/10.1016/J.RSE.2018.03.021 |
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Geology, mapping surface minerals, cuprite |
Iqbal, A., Ullah, S., Khalid, N., Ahmad, W., Ahmad, I., Shafique, M., … Skidmore, A. K. (2018). Selection of HyspIRI optimal band positions for the earth compositional mapping using HyTES data. Remote Sensing of Environment, 206, 350–362. https://doi.org/10.1016/J.RSE.2017.12.005 |
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Land cover |
Thompson, D. R., Boardman, J. W., Eastwood, M. L., & Green, R. O. (2017). A large airborne survey of Earth’s visible-infrared spectral dimensionality. Optics Express, 25(8), 9186. https://doi.org/10.1364/OE.25.009186 |
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Acid mine drainage |
Davies, G. E., & Calvin, W. M. (2017). Mapping acidic mine waste with seasonal airborne hyperspectral imagery at varying spatial scales. Environmental Earth Sciences, 76(12), 432. https://doi.org/10.1007/s12665-017-6763-x |
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Bark beetle outbreak, tree mortality |
Tane, Z., Roberts, D., Koltunov, A., Sweeney, S., & Ramirez, C. (2018). A framework for detecting conifer mortality across an ecoregion using high spatial resolution spaceborne imaging spectroscopy. Remote Sensing of Environment, 209, 195–210. https://doi.org/10.1016/J.RSE.2018.02.073 |
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Land cover, land use, Random forests, MESMA |
Clark, M. L. (2017). Comparison of simulated hyperspectral HyspIRI and multispectral Landsat 8 and Sentinel-2 imagery for multi-seasonal, regional land-cover mapping. Remote Sensing of Environment, 200, 311–325. https://doi.org/10.1016/J.RSE.2017.08.028 |
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Canopy optical reflectance, photosynthesis |
Serbin, S. P., Singh, A., Desai, A. R., Dubois, S. G., Jablonski, A. D., Kingdon, C. C., … Townsend, P. A. (2015). Remotely estimating photosynthetic capacity, and its response to temperature, in vegetation canopies using imaging spectroscopy. Remote Sensing of Environment, 167, 78–87. https://doi.org/10.1016/J.RSE.2015.05.024 |
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Biodiversity Phytoplankton functional type Harmful algal bloom Water quality Atmospheric correction |
Palacios, S. L., Kudela, R. M., Guild, L. S., Negrey, K. H., Torres-Perez, J., & Broughton, J. (2015). Remote sensing of phytoplankton functional types in the coastal ocean from the HyspIRI Preparatory Flight Campaign. Remote Sensing of Environment, 167, 269–280. https://doi.org/10.1016/J.RSE.2015.05.014 |
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Biodiversity Phytoplankton functional type Harmful algal bloom Water quality |
Palacios, S. L., Kudela, R. M., Guild, L. S., Negrey, K. H., Torres-Perez, J., & Broughton, J. (2015). Remote sensing of phytoplankton functional types in the coastal ocean from the HyspIRI Preparatory Flight Campaign. Remote Sensing of Environment, 167, 269–280. https://doi.org/10.1016/J.RSE.2015.05.014 |
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Terrestrial ecology Aquatic ecology |
Special issue on the Hyperspectral Infrared Imager (HyspIRI): Emerging science in terrestrial and aquatic ecology, radiation balance and hazards. (2015). Remote Sensing of Environment, 167, 1–5. https://doi.org/10.1016/J.RSE.2015.06.011 |
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Atmospheric correction |
Thompson, D. R., Gao, B.-C., Green, R. O., & Lundeen, S. R. (2015). Atmospheric correction for global mapping spectroscopy: ATREM advances for the HyspIRI preparatory campaign. Remote Sensing of Environment, 167, 64–77. https://doi.org/10.1016/J.RSE.2015.02.010 |